An Evaluation of Ontology Exchange Languages
for Bioinformatics
Authors:
Robin McEntire (SB)
Peter Karp (Pangea Systems)
Neil Abernethy (InGenuity)
Frank Olken (LBNL)
Robert E. Kent (WSU)
Matt DeJongh (NetGenics)
Peter Tarczy-Hornoch (U of Washington, Seattle)
David Benton (SB)
Dhiraj Pathak (GW)
Gregg Helt (UC Berkeley)
Suzanna Lewis (UC Berkeley)
Anthony Kosky (GeneLogic)
Eric Neumann (NetGenics)
Dan Hodnett (NetGenics)
Luca Tolda (Merck KGA)
Thodoros Topaloglou (GeneLogic)
August 1, 1999
Abstract
Ontologies are specifications of the concepts in a given field and the relationships
among those concepts. The development of ontologies for molecular-biology
information and the sharing of those ontologies within the bioinformatics
community are central problems in bioinformatics. If the bioinformatics
community is to share ontologies effectively, ontologies must be exchanged in a
form that uses standardized syntax and semantics. This paper reports on an effort
among the authors to evaluate a number of alternative ontology-exchange
languages, and to recommend one or more languages for use within the larger
bioinformatics community. The study selected a set of candidate languages, and
defined a set of capabilities that the ideal ontology-exchange language should
satisfy. The study scored the languages according to the degree to which they
provided each capability. In addition, the authors performed several ontologyexchange experiments with the two languages that received the highest scores:
OML and Ontolingua. The result of those experiments, and the main conclusions
of this study, was that the frame-based semantic model of Ontolingua is
preferable to the conceptual graph model of OML, but that the XML-based syntax
of OML is preferable to the Lisp-based syntax of Ontolingua.
1. Introduction
Ontologies, as specifications of the concepts in a given field and the relationships
among those concepts, provide insight into the nature of information produced by
that field and are an essential ingredient for any attempts to arrive at a shared
understanding of concepts in a field. Thus the development of ontologies for
molecular-biology information and the sharing of those ontologies within the
bioinformatics community are central problems in bioinformatics.
If the bioinformatics community is to share ontologies effectively, the ontologies
must be exchanged in some standardized form, such as using a file with a welldefined syntax and semantics. Exchange of bioinformatics ontologies will be
simplified if the community can agree on a relatively small number of such
exchange forms --- ideally, on one form.
This paper reports on an effort among the authors to evaluate a number of
alternative ontology-exchange languages, and to recommend one or more
languages for use within the larger bioinformatics community. The evaluation
effort involved three separate meetings in 1998 and 1999 by the authors, as well
as experiments with the proposed ontology languages. In phase I of the
evaluation, the authors selected a set of candidate languages, and a set of
capabilities that the ideal ontology-exchange language should satisfy.
The authors then scored the languages according to the degree to which they
provided each capability. In phase II of the evaluation, the authors performed
several ontology-exchange experiments with the two languages that rated the
highest during phase I, which were OML and Ontolingua.
This paper describes the evaluation process and its results in more detail.
A web site maintained by the authors can be found at http://wwwsmi.stanford.edu/projects/bio-ontology/.
2. Motivations
This section discusses the motivations for this work in more detail.
Ontology development is important because every biological database employs an
ontology, either implicitly or explicitly, to model its data. The more fine-grained
the ontology, the more precisely the database will be able to model the nuances of
the data that it tries to capture. A coarse-grained ontology will model only
superficial aspects of the data, and therefore may not capture data elements that
are important for some problem-solving task. For example, a genome-sequence
database that fails to record which genetic code is used to encode a given DNA
sequence does not provide the information that users of the database will need to
reliably translate each DNA sequence into the corresponding protein sequence. A
semantically malformed ontology is one that incorrectly models the semantics of
its application domain, and therefore yields a database whose structure corrupts or
restricts the information that it is intended to hold. For example, a metabolic
database that defines a one-to-one relationships between enzymes and the
reactions they catalyze cannot reliably model the fact that a bifunctional enzyme
catalyzes two separate reactions.
Ontology sharing is important for a number of reasons. First, ontology
development is time consuming. Different bioinformatics groups who wish to
develop ontologies for the same types of biological information will often arrive
at a solution faster by adopting an existing ontology than by developing a new
ontology de novo. For example, a group that wishes to define an ontology for
microarray gene-expression data will almost certainly accomplish this task more
quickly by consulting one or more existing microarray ontologies.
Second, if different bioinformatics databases that cover the same types of data
(e.g., protein sequences) employ the same ontology, they simplify the problem of
database integration, i.e., of processing queries across multiple biological
databases. Different ontologies for the same types of data produce a semantic
mismatch that complicates the multidatabase query problem.
Third, bioinformatics databases must make their schemas available to their user
communities if the users are to have a full understanding of the semantics of these
databases.
Fourth, ontology sharing is important because ontologies themselves constitute a
form of biological knowledge that is quite valuable when shared within the
bioinformatics community. For example, the taxonomy of enzymatic reactions
developed by the Enzyme Commission {EC}, and the taxonomy of gene function
developed by Riley {RileyOntol} are valuable bioinformatics ontologies.
Fifth, differences between ontologies puporting to represent the same biological
process may lead to important insights into ways of improving those
representations, and/or new insights into the underlying biology.
3. Terminology
Ontologies are defined in the literature in a number of ways with varying degrees
of formality. One prevailing definition of an ontology is a specification of a
conceptualization that is designed for reuse across multiple applications. By
conceptualization, we mean a set of concepts, relations, objects, and constraints
that define some domain of interest.
One can argue at length about what is and is not an ontology {Gruber,Guarino}.
Our view is that ontologies exist at several levels of complexity:
 A controlled vocabulary is an ontology that simply lists a set of terms.
 A taxonomy is a set of terms that are arranged into a generalizationspecialization hierarchy. A taxonomy does not define attributes of these
terms, nor does it define relationships between the terms.
 An object-oriented database schema defines a hierarchy of classes, and
attributes and relationships of those classes.
 A knowledge-representation system based on first-order logic can express all
of the preceding relationships, as well as negation and disjunction.
The GeneClinics experiment (see www.geneclinics.org) illustrates this range of
complexity among different ontologies. One of the first steps of the experiment
was to augment the object-oriented schema with a richer set of capabilities
including disjunction, role restriction and other constraints. In the GeneClinics
object database much of this information was in fact represented in the Java
software interacting with the database but was hidden from the end user.
4. Candidate Languages
In this section we will discuss the candidate ontology-exchange languages that
were evaluated by the authors. We discuss the reasons each language was
selected for consideration as a bioinformatics ontology exchange language, we list
the developers of each language and the reasons for its development, and provide
references for each language.
4.1. Ontolingua
The Ontolingua language was developed by a group at Stanford University for the
exchange of ontologies, and was originally funded by the DARPA Knowledge
Sharing Effort (Ref). Ontolingua is one of the most significant efforts to come
out of the knowledge representation community and is based on the Knowledge
Interchange Format (KIF), a language specifically built for the sharing of
knowledge among different knowledge representation systems. The authors
believed that any evaluation of languages for the exchange of ontologies must
include this project.
The semantics of Ontolingua are based on the frame knowledge representation
systems developed by knowledge-representation researchers
{Fikes,KarpReview}.
4.2. CycL
Cyc is perhaps the best-known of the knowledge representation systems and is
significant in its scope and its longevity. Cyc was developed by Doug Lenat at
MCC but has since spun-off as a commercial entity, Cycorp. The underlying
representation language for Cyc is called CycL, which derives from first-order
predicate calculus but with extensions for additional expressivity. Cyc is one of
the most significant commercial products, if not the most significant, in the
marketplace currently. For this reason, as well as it's significance within the
knowledge representation community and the rich expressive abilities, it was
selected for evaluation.
4.3. OML/CKML
Ontology Markup Language/Conceptual Knowledge Markup Language
(OML/CKML) is a relatively new effort coming out of Washington State
University that is attempting to base a system for the expression of ontologies on
an XML-based syntax. The OML effort was begun in the 1990's and, though
relatively young and untested, the authors believed it to have a significant
representational power. This representational power combined with the
interoperable nature of an XML-based language was believed to be a combination
worth investigating. In addition, since OML/CKML is currently under
development there is a potential for co-development to allow the bioinformatics
community to influence features and expressive power of the language. There is,
though, a possible disadvantage in that the language may evolve in ways that are
not to the advantage of the community or is perhaps not stable or standardized.
4.4. OPM
OPM was interesting to the authors as a candidate language for exchange of
ontologies because of the significance of the OPM system, a product from
GeneLogic used in a number of Pharmaceutical/BioTech organizations. OPM, as
a product, is used for the integration of multiple information sources, and uses an
underlying object-oriented federated schema for this purpose.
4.5. XML/RDF
Extensible Markup Language/ Resource Description Format (XML/RDF) were
developed by the W3C. The current standard for the XML Schema Language is
controlled by the XML Schema Working Group of the W3C. (RDF) is intended to
encode metadata concerning web documents. XML/RDF were investigated as a
part of the evaluation effort because of the significance of the web and web-based
applications. It is clear that the web is rapidly becoming the primary method for
the exchange of information and data, and that XML is currently the leading
candidate for a generic language for the exchange of semi-structured objects.
XML/RDF as is, without a higher level formalism that encompasses the
expressivity present in frame-based languages does not go far enough to allow the
kind of modeling needed in the bioinformatics community.
4.6. UML
The Unified Modeling Language (UML) provides a set of notational conventions
that can be used by software application designers/developers to model their
software system. UML was developed by Rational Software and is currently
backed by Rational, Microsoft and the OMG. UML was selected for evaluation
because it is another widely-used system for the representation of objects and
their relationships.
4.7. OKBC
The Open Knowledge Base Connectivity (OKBC) is an API for accessing and
modifying multiple, heterogeneous knowledge bases. OKBC is not actually an
ontology exchange language – it is a programmatic API. This group considered it
because its knowledge model was designed to capture ontologies. The OKBC
effort began as a part of the recent DARPA High Performance Knowledge Base
(HPKB) program, and is the successor of Generic Frame Protocol (GFP), a frame
representation system developed at the Artificial Intelligence Center at SRI.
OKBC was created because it provides a uniform model that can be understood
across a number of knowledge representation systems. The work on OKBC is
currently being overseen by a working group lead by Richard Fikes at Stanford.
Voting members in this group are; ISI, Stanford KSL, SRI International, Cycorp,
SAIC and Teknowledge.
4.8. ASN.1
ASN.1 was included in this evaluation because of it's historical significance as an
early language for the exchange of datatypes and simple objects. The ASN.1
standard was developed as part of the OSI networking stack. It has been, and still
is, being used in a number of bioinformatics applications from the National
Center for Biotechnology Information. ASN.1 was also used in conjunction with
the Unified Medical Language System (UMLS) project at the National Library of
Medicine (NLM), however, production of ASN.1 encodings of the UMLS has
been discontinued because of low demand for ASN.1 by UMLS users.
4.9. ODL
The Object Definition Language (ODL) is a relatively new standard coming out
of the Object Database Management Group (ODMG) in the early 1990's. ODL
was selected for evaluation because it is currently a de facto standard for a
common representation of objects for object-oriented databases and programming
languages and so has the potential to become a standard supported widely
throughout the industry. The ODMG member companies include almost all
organizations in the ODBMS/ODM industry and is very closely aligned with the
OMG.
5. Evaluation
5.1. Evaluation Part I: Initial Evaluation
Selection of Candidate Languages
The evaluation process began with the selection of known languages for
expressing ontologies. Our selection process relied on an informal review of
current literature and prior knowledge of participants, but, we believe, covers the
most viable candidate languages for the exchange of ontologies. The languages,
once selected, were then divided among the authors for evaluation.
Selection of Evaluation Criteria
In order to evaluate the languages in a consistent fashion the authors arrived at a
set of questions over which each candidate language would be evaluated. The
questions that were distributed to members of the working group is included in
Appendix A. The questions were divided into the following five major
categories;
1. Language Support and Standardization: This section includes general
questions about the depth of support for the language, including technical
support and relationship with standards efforts
2. Data model/capabilities: This section asks about the richness of the expressive
capabilities of the language.
3. Querying: This section poses questions about the capabilities of query
languages available for a representation language.
4. Performance: Though not related to issues of the expressiveness of the
language, the authors wanted to capture some notion of what might be
expected in terms of performance if we were to use a given language.
5. Other Issues: This section is more concerned with pragmatics, such as current
use of the language and representation of, or connectivity to, non-ontology
sources.
Evaluation Matrix
The final judgement of the authors for the initial evaluation phase was guided by a
matrix of the aspects of an exchange language that were considered key to it's use
by members of the Bio-Ontology Consortium (http://wwwsmi.stanford.edu/projects/bio-ontology/) and other groups who may want to build
ontologies in the area of molecular biology. This evaluation matrix is included in
Appendix B.
Selection of Languages for further Evaluation
The authors decided that there was not a single language that stood out as the only
appropriate candidate for recommendation as a language for the exchange of
ontologies. It was clear that representational expressiveness was not adequate in
some languages, and so they were eliminated from consideration. For example,
some languages were unable to encode ground facts (instance objects). Also,
some languages were in part or in whole proprietary, or had a significant cost
associated with them. This was considered prohibitive to the successful adoption
and use of the languages and so these languages were also eliminated . It was
decided that two languages, Ontolingua and OML/CKML, provided enough
expressivity to warrant a more in-depth evaluation.
5.2. Evaluation Part II: OML and Ontolingua
The second phase of the evaluation process focused on the two candidate
languages that were deemed most interesting from the initial evaluation:
Ontolingua and OML/CKML.
The authors decided that it would be useful to create a small model in each
language in order to judge the utility and the representational richness of each
language. A set of experiments were developed to perform this detailed
evaluation. Three sets of experimenters were undertaken. The three experiments
and their results are discussed below.
OML Representation of the EcoCyc Gene Ontology
Experiment:
Peter Karp's group at Pangea Systems performed an experiment to better
understand the OML language by translating the EcoCyc gene ontology into
OML. The gene ontology is a taxonomy of 150 classes that classify microbial
genes according to their functions, and that was developed by Dr. Monica Riley
as part of the EcoCyc project.{Riley,EcoCyc}
Within EcoCyc, the ontology can be accessed at
http://ecocyc.panbio.com:1555/class-subs?object=Genes The OML encoding of
the ontology can be accessed at: http://ecocyc.panbio.com/~pkarp/omlgenes.txt
Results:
Our findings were that OML was able to capture most aspects of the gene
ontology. However, we identified what we consider to be a number of limitations
of OML during the course of this experiment.
1. A number of aspects of the terminology used in the tags in OML files are not
at all intuitive, and are not consistent with the terminology used in the more
mainstream ontology community. This terminology will interfere with the
acceptance and understanding of the language in the bioinformatics
community. We suggested that OML could allow several alternatives for each
tag to allow the language to be accepted by different communities that use
different terminology.
2. The OML definitions are not modular in the sense that the OML definition of
a given Class is spread out into several parts of the file, making OML files
less human readable.
3. OML has a number of limitations in its expressive power:
a) It cannot express facets directly (attributes of attributes), but R. Kent
suggested that N-ary relations can be used to express facets.
b) It cannot express annotations.
c) It cannot handle multiple collection types -- sets only.
d) It cannot express cardinality or numeric-range constraints.
Ontolingua Representation of the EcoCyc Gene Ontology
Experiment:
Results:
Representation of GeneClinics data model as an ontology
Experiment:
Peter Tarczy-Hornoch at the University of Washington in collaboration with Luca
Toldo and Robert Kent performed an experiment with the general goal of using
the existing GeneClinics OODB model as the basis for an ontology to assess
OML/CKML and Ontolingua for ontology creation/exchange. The specific goal
was to develop a small representative ontology in both Ontolingua and
OML/CKML that represents key clinical and molecular entities and their
linkages. design of the experiment was:
1. A distributed e-mail based experiment involving three investigators at three
sites
2. The GeneClinics investigator developed 5 page document outlining subset of
high level (coarse grain) GeneClinics OODB model. The scope of this model
was to represent key clinical entities (clinical diagnoses, tests), key molecular
entities (genes, loci, products, alleles, mutations), and their inter-relationships
(causality maps to diagnoses, clinical tests for molecular entities)
3. The whole group clarified points including disjunctions, restrictions, other
constraints not in OODB model
4. The developer of OML/CKML (one of the three participants)
implemented/refined OML/CKML ontology
5. A specific instance (Charcot Marie Tooth type 1A) was represented
6. The same ontology was represented in parallel in Ontolingua
7. A specific instance (CMT 1A) was represented in Ontolingua
8. The OML/CKML and Ontolingua experiences were compared and contrasted
9. A handful of very granular elements were implemented (chosen to “stress”
each language and compare robustness)
Results:
1. The underlying paradigms of Ontolingua and OML/CKML are subtly
different – frames based vs. conceptual graph based (formal concept analysis,
information flow theory). Both require effort to learn the paradigm if you are
not familiar with it.
2. Ontolingua concepts mapped more closely to object databases and object
oriented programming paradigms -- thus might be easier for typical
bioinformaticist to learn.
3. Minor difference in namespace -- Ontolingua requires name to be a unique
identifier.
4. OML/CKML’s XML syntax makes it easier to learn than Ontolingua with its
LISP syntax.
5. Neither language has the type of documentation of its syntax and semantics
that would be needed for a tutorial for a bioinformaticist. Ideally the
tutorial/documentation would need to include both formal representation of
syntax with modified BNF format as well as selected examples drawn from
biology building in complexity. For example, how do you represent a
biological entity like a protein, how do you express the concept that a
sequence of DNA codes for that protein, how do you express that proteins
have one or more of the following list of functions, etc.
6. Both languages very expressive – Ontolingua’s expressivity is easier to see in
both LISP and in the Ontolingua ontology-development tool because it is
exposed even in simple case examples. OML/CKML expressivity is rich but
harder to determine since a) it is not apparent is simpler examples, b) things
like local theories and other concepts are powerful but harder to understand
(documentation in conceptual graph paradigm, documentation and
specification both evolving). In principle the OML/CKML conceptual graph
model may be richer and more expressive than the frame model; an exact
comparison of the two models would be useful.
7. Both languages able to handle needs of GeneClinics sample ontology (not a
complex ontology).
8. Conceptual graph paradigm dense but very powerful (see document
Designator-Facet.doc for examples).
9. Though not per se an attribute of the languages themselves it is important to
note that software tools and applications, such as editors, browsers, parsers,
translators, and query systems, exist for Ontolingua but not for OML/CKML.
10. OML/CKML is "an uninstantiated formalism" at some level.
11. The availability of the developer of OML/CKML (R. Kent) for collaboration
on this project was immensely helpful.
Conclusions: The expressive power of the two languages is similar and more than
adequate for the purposes of expressing a part of the GeneClinics data model as
an ontology. OML/CKML is however theoretically more powerful being based on
a conceptual graph methodology. The Ontolingua frames semantics/paradigm on
the other hand may be easier to learn since it is less of a leap from object database
and object programming paradigms. The LISP syntax of Ontolingua could present
a challenge to many bioinformaticians and the XML syntax of OML/CKML is
likely to be more intuitive. Ideally an ontology exchange language would have an
easy to learn basic semantics and syntax (like XML) but be very expressive (like
OML/CKML and Ontolingua). Neither language as it stands quite achieves this
ideal though a more frame-based version of OML/CKML or an XML encoding of
Ontolingua might come closer. Finally, for the general bioinformatics community
(not versed in ontology representation) it might be helpful to create
documentation and tutorials that use biological examples.
5.3. Evaluation Part III: Recommendations
At its last meeting, the BioOntology Core Group reached the following
conclusions and recommendations.
The core group reached two major decisions for the selection of a language for the
exchange of ontologies for molecular biology:
1) A traditional frame-based approach for representation of biological entities is
sufficient for current needs. In addition, frame-based systems have been in
use for a significant period of time and are, in general, stable representation
systems. Among frame-based systems Ontolingua is clearly one of the most
prominent and has had extensive use for many years.
2) XML has tremendous momentum with significant interest from commercial
organisations and a serious standardisation effort. We anticipate that XMLbased tools and web servers supporting XML are beginning to appear and
more are on the horizon.
The belief of the group was that the language that the bioinformatics community
needs for the exchange of ontologies should be based on frame-based semantics
with an XML expression. However, the group also believed that we did not have
such a language before us since Ontolingua is frame-based but without an XML
expression and OML does have an XML expression, but is based on conceptual
graphs, not frames.
At the meeting Peter Karp presented preliminary work that he and Vinay
Chaudhri, from SRI, had done on producing an XML expression based on the
OKBC knowledge model, which in turn is very closely related to Ontolingua (the
Ontolingua developers were also involved in the development of OKBC).
The consensus of the group was that we recommend the use of a frame-based
language with an XML syntax for the exchange of ontologies, and, to that end, the
group requested that Karp and Chaudhri complete their work on the XML
expression of Ontolingua, so that the group could complete its evaluation of
exchange languages.
6. Summary
Over the last two decades, the knowledge representation and object-oriented
database communities have developed a number of languages that may be used
for the expression of semantic database models. These languages share many
elements in common, and are exemplified by the frame knowledge representation
systems used in the knowledge representation community. Frame systems have
been used in many different bioinformatics projects, and the authors believe that
frame systems provide the necessary representational constructs to model
ontologies for molecular biology. Furthermore, frame systems have a significant
amount of history and use, so that they provide a stable representational paradigm.
The authors also believe that the explosion of the web and the languages
associated with it simply cannot be ignored. Acceptance of an exchange language
that is expressed in a Lisp syntax will be limited within the bioinformatics
community, even though the underlying representational system may be identical
to that expressed in a web-based language. For this reason the authors believe
that an XML-based syntax must be used for a bioinformatics ontology exchange
language to increase the likelihood that the language will see widespread
acceptance.
In summary, the results of this evaluation suggest two directions for future work:
development of an XML expression for the Ontolingua model, or adapting
OML/CKML to include a frame-based semantic model.
7. Future Directions
The authors support the use of a frame-based exchange language using an XML
syntax. Several researchers on the evaluation team are currently developing a
specification of XML expression of Ontolingua using OKBC. A separate set of
researchers on the team are pursuing a frame-based version of OML.
The exchange language evaluation team will meet again to consider the question
of whether either, or both, of these efforts provide an acceptable exchange
language that meets the groups requirements.
References
EC
Edwin C. Webb, "Enzyme Nomenclature, 1992: Recommendations of the
nomenclature committee of the International Union of Biochemistry and
Molecular Biology on the nomenclature and classification of enzymes",
Eur. J. Biochem., Academic Press, 1992.
Fikes
Fikes, R. and Kehler, T., "The Role of Frame-Based Representation in
Reasoning", Communications of the Association for Computing Machinery,
1985, 28(9):904-920.
Gruber
Gruber, T.R., "A translation approach to portable ontology
specifications", Knowledge Acquisition, 1993 5:199-220.
Guarino
Guarino, N. and Giaretta, P., "Ontologies and knowledge bases towards
a terminological clarification", in Towards very large knowledge
bases, IOS Press, Amsterdam, 1995, N.J.I. Mars, pp25-32.
KarpReview
Karp, P.D., "The design space of frame knowledge representation
systems", SRI International AI Center, 1992, #520, URL
ftp://www.ai.sri.com/pub/papers/ karp-freview.ps.Z.
RileyOntol
Riley, M., "Functions of the gene products of Escherichia
coli", Microbiological Reviews, 1993, 57:862-952.
8. Appendix A - Evaluation Questions
The following questions were asked of each candidate language during the Phase
I evaluation process.
Language Support and Standardization:
1. Is a formal specification of the syntax of the ontology language available?
How complex is its syntax? Please present that formal specification of the
language at the meeting.
2. What parsers are available for the language? What translators are available to
convert between language L and other ontology-description languages? How
complete are those translators?
3. What other software is available that operates on the language, such as for
web-based publishing of ontologies or browsing/editing of ontologies?
4. What support (documentation, training, tutorials, e-mail) is available for the
language?
5. Does it have any development/usage standards? Who controls this standard?
6. Does a stable release of the language exist (i.e. one that will not
fundamentally change in 6 months)?
Data model/capabilities:
1. What assumptions does the language make about the ontology to be
represented?
2. Which of the following does the language support:
 negation
 conjunction
 disjunction
 recursion
 relations
 multiple inheritance
 multi-valued slots
 number restrictions on roles
 role hierarchies
 transitive roles
 axioms
 template/default values
3.
4.
5.
6.
 method slots (calculated values?)
 constraints
If the language supports constraints, how rich is the constraint language? Is
the constraint language formally defined?
What are the primitive data types in the language?
What database data model(s) does the language support?
Does the language encode instances as well as classes (data as well as
schema?)
Querying:
1. Are there tools for query an ontology expressed in this language? If so, ...
2. How are queries expressed?
3. Which of the following queries can be expressed in the query language:
 what are the parents of concept C?
 what are the children of concept C?
 what could I say about concept C (e.g. what roles are legally applicable to
C)?
 is concept C satisfiable?
 what role-fillers can a role have for a concept C?
 what English expression does C have?
 is C a kind of D?
 what is the least common parent of C and D
 what is the greatest common child of C and D
 are C and D equivalent?
 Can queries be translated/compiled into a standard programming/query
language?
Performance:
[These questions are more about ontology tools (editors, viewers, ...) than
language.]
1. Are there any limits (or the limits of available translators/parsers) in the size
of the ontology, the length of names/values, etc. (theoretical or practical).
2. What is the overhead (bytes) for a language parser? interpreter?
3. For resources which depend on an information service for support (such as
Ontolingua), does the service have the capacity to support all of the users of
the technology?
Other Issues:
1. What example applications exist which utilize the language? How many of
these are from or representative of the bioinformatics domain?
[The two questions below are asking about the ability to express non-domain
relevant information in the ontology, so that, for example, one could include user
model information (preferences for viewers, etc) or database access information
(for access to persistent instance-level information) in the domain model.]
2. Can the ontology be partitioned, for example, into biology and bioinformatics
(e.g. a protein has an accession number)?
3. Can the core ontology be extended to include other information, e.g. mappings
to functions in databases, control information for showing the ontology
through interfaces
9. Appendix B - Evaluation Matrix, Part I
The table below shows the evaluation of candidate languages over general
information.
Property ASN.1 ODL
Onto
OML/
OPM
XML/
UML
CKML
RDF
Formal
Yes
No
Yes
Yes
Yes
Yes
Yes
Syntax?
Translato
No
Yes
Loom,I
No
Relatio
No
No
rs
DL,KIF
nal,AS
,CLIPS,
N.1,XM
etc
L,HTM
L,ER
Software
Parser Parsers WWW
No
Yes
XML
Rationa
Tools
s
browser
toolkits l Rose
s,editor
s,compa
rison
tools
Support
??
yes
WWW WWW Docum WWW Formal
docume gramma entation
sites,
courses,
ntation,
rs,
,trainin mailing books,
FAX,tut WWW g,tutori
lists,
tutorials
orial,su exampl
als
books
pport
es
staff
Controlli
ng Org
Stability
Users
Bioinfo
Users
Develope
rs
ISO
ODMG
Stable
Stable
Yes
OO
Vendor
s
Yes
NCBI
??
OO
Vendor
s
Stanfor
dU
Stable
WWW
users
WSU
Evolvin
g
Intel
apps
SB,
Stanfor
d
RoboW
eb
Yes
Stanfor
d
WSU
GeneLo
gic Inc
Stable
W3C
OMG
Stable
Yes,
Bix and
others
PDB
Evolvin
g
WWW
develop
ers
No
GeneLo
gic
many,
many
many
parts of
industry
SB,
(probab
ly many
other
pharma
s)
Rationa
l Rose
10. Appendix C - Evaluation Matrix, Part II
The table below shows the results of evaluation over detailed properties of the
representational expressiveness of candidate languages.
Property ASN.1 ODL
Onto
OML/
OPM
XML/
UML
CKML
RDF
Negation
No
No
Yes
Yes
??
No
No
Conjuncti
No
No
Yes
Yes
??
No
No
on
Disjuncti
No
No
Yes
Yes
??
No
No
on
Relations
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Multiple
No
Yes
Yes
Yes
Yes
Yes
No
Inheritan
ce
Inverses
No
Yes
Yes
Yes
Yes
No
No
MultiYes
Yes
Yes
Yes
Yes
Yes
No
valued
slots
Multiple
Yes
Yes
No
No
??
Yes
No
collection
types
Number
restrictio
ns
Slot
hierarchie
s
Facets
Default
Values
Other slot
constraint
s
Primitive
Datatypes
Data
Model
Instances
and
classes
No
No
Yes
Yes
??
No
Yes
No
No
Yes
Yes
??
No
No
No
No
No
No
Yes
No
Yes
Yes
??
??
No
Yes
No
No
No
No
Yes
Yes
No
No
No
Standar
d
Object/
Logic
Standar
d
Object/
Logic
Standar
d
Object
None
N/A
SemiStr
uctured
data
Object
Yes
Yes
No
Yes
No
Standa Standar
rd
d
Object Object
w/o
inherit
ance
No
No
Comparison of the expressive power of the ontology-exchange
languages. The meanings of the rows are:
1. Negation: Does the language allow the assertion that a relation does not hold
between x and y.
2. Conjunction: Does the language allow the assertion that a relation holds both
between (x, y) and between (x, z).
3. Disjunction: Does the language allow the assertion that a relation holds both
between (x, y) or between (x, z), but not both.
4. Relations: Does the language allow the mapping of the elements of a set A to
the elements of a set B.
5. Multiple inheritance: Can the language describe inheritance of a child class
from multiple parent classes?
6. Inverses: Can the language encode that slot X and slot Y are inverses of one
another?
7. Multi-valued slots: Can the language encode slots that may have multiple
values?
8. Multiple collection types: Can the language encode slots with different
collection types such as bags, sets, and sequences?
9. Number restrictions on slots: Can the language encode constraints on the
number of values a slot may have?
10. Slot hierarchies: Can the language encode taxonomic hierarchies of slots?
11. Facets: Can the language encode facets (facets encode properties of slots)?
12. Default values: Can the language encode default slot values?
13. Other slot constraints: Can the language encode other types of constraints on
slot values, such as numeric ranges?
14. Primitive datatypes: What primitive datatypes does the language support?
``Standard'' indicates standard datatypes such as numbers, strings, Boolean.
15. Data model: What database data model does the language support?
16. Instances and classes: Can the language encode information about instance
objects as well as class objects?
Appendix D - Evaluation Matrix
The table below was used by the authors to evaluate the initial candidate
languages after our evaluations over the questions was complete. This table
shows the desired attributes of an exchange language, and how each language can
be rated along those aspects. A plus sign, '+', indicates a positive. More than one
plus sign indicates more significant positives. The minus sign, '-', indicates a
negative evaluation of a criteria. Also, AF indicates that the language/product is
free to academic organizations, and
classes &
instances
multiple
inheritance
constraints
defaults
expressive power
tools available*
Onto XML OML OKBC
/RDF
+
+
+
+
+
+
+
+
++
++
+
+
+
+
+
+++
+ +++
++
lisp Java
lisp, Java,
(AF)
C
stability
+
+
+
support
+
++
+
+
translators
++
+
?
+
many applications
+
+
+
+
open language
+
+
+
+
OPM
CycL
-
+
UML
/XMI
+
+
+
+
+
+
++
Java,
C++
+
+
+
+
+
+
+
+++
lisp, Java,
C (AF)
+
+
KIF. Loom
+
+
+
+
+
simplicity: human good low low
simplicity: formal good good good
open to
+
++ ++
collaboration
good
good
good
good
low
+
STATUS
out
out
out
out
out
* By "tools available" the authors mean browsers and editors for the language.